Publications

2017
Jose de Arcos, Ehud J Schmidt, Wei Wang, Junichi Tokuda, Kamal Vij, Ravi T Seethamraju, Antonio L Damato, Charles L Dumoulin, Robert A Cormack, and Akila N Viswanathan. 2017. “Prospective Clinical Implementation of a Novel Magnetic Resonance Tracking Device for Real-Time Brachytherapy Catheter Positioning.” Int J Radiat Oncol Biol Phys, 99, 3, Pp. 618-26.Abstract
PURPOSE: We designed and built dedicated active magnetic resonance (MR)-tracked (MRTR) stylets. We explored the role of MRTR in a prospective clinical trial. METHODS AND MATERIALS: Eleven gynecologic cancer patients underwent MRTR to rapidly optimize interstitial catheter placement. MRTR catheter tip location and orientation were computed and overlaid on images displayed on in-room monitors at rates of 6 to 16 frames per second. Three modes of actively tracked navigation were analyzed: coarse navigation to the approximate region around the tumor; fine-tuning, bringing the stylets to the desired location; and pullback, with MRTR stylets rapidly withdrawn from within the catheters, providing catheter trajectories for radiation treatment planning (RTP). Catheters with conventional stylets were inserted, forming baseline locations. MRTR stylets were substituted, and catheter navigation was performed by a clinician working inside the MRI bore, using monitor feedback. RESULTS: Coarse navigation allowed repositioning of the MRTR catheters tips by 16 mm (mean), relative to baseline, in 14 ± 5 s/catheter (mean ± standard deviation [SD]). The fine-tuning mode repositioned the catheter tips by a further 12 mm, in 24 ± 17 s/catheter. Pullback mode provided catheter trajectories with RTP point resolution of ∼1.5 mm, in 1 to 9 s/catheter. CONCLUSIONS: MRTR-based navigation resulted in rapid and optimal placement of interstitial brachytherapy catheters. Catheters were repositioned compared with the initial insertion without tracking. In pullback mode, catheter trajectories matched computed tomographic precision, enabling their use for RTP.
Elmira Hassanzadeh, Daniel I Glazer, Ruth M Dunne, Fiona M Fennessy, Mukesh G Harisinghani, and Clare M Tempany. 2017. “Prostate Imaging Reporting and Data System Version 2 (PI-RADS v2): A Pictorial Review.” Abdom Radiol (NY), 42, 1, Pp. 278-89.Abstract

The most recent edition of the prostate imaging reporting and data system (PI-RADS version 2) was developed based on expert consensus of the international working group on prostate cancer. It provides the minimum acceptable technical standards for MR image acquisition and suggests a structured method for multiparametric prostate MRI (mpMRI) reporting. T1-weighted, T2-weighted (T2W), diffusion-weighted (DWI), and dynamic contrast-enhanced (DCE) imaging are the suggested sequences to include in mpMRI. The PI-RADS version 2 scoring system enables the reader to assess and rate all focal lesions detected at mpMRI to determine the likelihood of a clinically significant cancer. According to PI-RADS v2, a lesion with a Gleason score ≥7, volume >0.5 cc, or extraprostatic extension is considered clinically significant. PI-RADS v2 uses the concept of a dominant MR sequence based on zonal location of the lesion rather than summing each component score, as was the case in version 1. The dominant sequence in the peripheral zone is DWI and the corresponding apparent diffusion coefficient (ADC) map, with a secondary role for DCE in equivocal cases (PI-RADS score 3). For lesions in the transition zone, T2W images are the dominant sequence with DWI/ADC images playing a supporting role in the case of an equivocal lesion.

Chantal MW Tax, Carl-Fredrik Westin, Tom Dela Haije, Andrea Fuster, Max A Viergever, Evan Calabrese, Luc Florack, and Alexander Leemans. 2017. “Quantifying the Brain's Sheet Structure with Normalized Convolution.” Med Image Anal, 39, Pp. 162-77.Abstract
The hypothesis that brain pathways form 2D sheet-like structures layered in 3D as "pages of a book" has been a topic of debate in the recent literature. This hypothesis was mainly supported by a qualitative evaluation of "path neighborhoods" reconstructed with diffusion MRI (dMRI) tractography. Notwithstanding the potentially important implications of the sheet structure hypothesis for our understanding of brain structure and development, it is still considered controversial by many for lack of quantitative analysis. A means to quantify sheet structure is therefore necessary to reliably investigate its occurrence in the brain. Previous work has proposed the Lie bracket as a quantitative indicator of sheet structure, which could be computed by reconstructing path neighborhoods from the peak orientations of dMRI orientation density functions. Robust estimation of the Lie bracket, however, is challenging due to high noise levels and missing peak orientations. We propose a novel method to estimate the Lie bracket that does not involve the reconstruction of path neighborhoods with tractography. This method requires the computation of derivatives of the fiber peak orientations, for which we adopt an approach called normalized convolution. With simulations and experimental data we show that the new approach is more robust with respect to missing peaks and noise. We also demonstrate that the method is able to quantify to what extent sheet structure is supported for dMRI data of different species, acquired with different scanners, diffusion weightings, dMRI sampling schemes, and spatial resolutions. The proposed method can also be used with directional data derived from other techniques than dMRI, which will facilitate further validation of the existence of sheet structure.
KT Huang, S Ludy, D Calligaris, IF Dunn, E Laws, S Santagata, and NYR Agar. 2017. “Rapid Mass Spectrometry Imaging to Assess the Biochemical Profile of Pituitary Tissue for Potential Intraoperative Usage.” Adv Cancer Res, 134, Pp. 257-82.Abstract

Pituitary adenomas are relatively common intracranial neoplasms that are frequently treated with surgical resection. Rapid visualization of pituitary tissue remains a challenge as current techniques either produce little to no information on hormone-secreting function or are too slow to practically aid in intraoperative or even perioperative decision-making. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) represents a powerful method by which molecular maps of tissue samples can be created, yielding a two-dimensional representation of the expression patterns of small molecules and proteins from biologic samples. In this chapter, we review the use of MALDI MSI, its application to the characterization of the pituitary gland, and its potential applications for guiding the management of pituitary adenomas.

Ma Luo, Sarah F Frisken, Jared A Weis, Logan W Clements, Prashin Unadkat, Reid C Thompson, Alexandra J Golby, and Michael I Miga. 2017. “Retrospective Study Comparing Model-Based Deformation Correction to Intraoperative Magnetic Resonance Imaging for Image-Guided Neurosurgery.” J Med Imaging (Bellingham), 4, 3, Pp. 035003.Abstract
Brain shift during tumor resection compromises the spatial validity of registered preoperative imaging data that is critical to image-guided procedures. One current clinical solution to mitigate the effects is to reimage using intraoperative magnetic resonance (iMR) imaging. Although iMR has demonstrated benefits in accounting for preoperative-to-intraoperative tissue changes, its cost and encumbrance have limited its widespread adoption. While iMR will likely continue to be employed for challenging cases, a cost-effective model-based brain shift compensation strategy is desirable as a complementary technology for standard resections. We performed a retrospective study of [Formula: see text] tumor resection cases, comparing iMR measurements with intraoperative brain shift compensation predicted by our model-based strategy, driven by sparse intraoperative cortical surface data. For quantitative assessment, homologous subsurface targets near the tumors were selected on preoperative MR and iMR images. Once rigidly registered, intraoperative shift measurements were determined and subsequently compared to model-predicted counterparts as estimated by the brain shift correction framework. When considering moderate and high shift ([Formula: see text], [Formula: see text] measurements per case), the alignment error due to brain shift reduced from [Formula: see text] to [Formula: see text], representing [Formula: see text] correction. These first steps toward validation are promising for model-based strategies.
Mukund Balasubramanian, William M Wells, John R Ives, Patrick Britz, Robert V Mulkern, and Darren B Orbach. 2017. “RF Heating of Gold Cup and Conductive Plastic Electrodes during Simultaneous EEG and MRI.” Neurodiagn J, 57, 1, Pp. 69-83.Abstract
PURPOSE: To investigate the heating of EEG electrodes during magnetic resonance imaging (MRI) scans and to better understand the underlying physical mechanisms with a focus on the antenna effect. MATERIALS AND METHODS: Gold cup and conductive plastic electrodes were placed on small watermelons with fiberoptic probes used to measure electrode temperature changes during a variety of 1.5T and 3T MRI scans. A subset of these experiments was repeated on a healthy human volunteer. RESULTS: The differences between gold and plastic electrodes did not appear to be practically significant. For both electrode types, we observed heating below 4°C for straight wires whose lengths were multiples of ½ the radiofrequency (RF) wavelength and stronger heating (over 15°C) for wire lengths that were odd multiples of ¼ RF wavelength, consistent with the antenna effect. CONCLUSIONS: The antenna effect, which has received little attention so far in the context of EEG-MRI safety, can play as significant a role as the loop effect (from electromagnetic induction) in the heating of EEG electrodes, and therefore wire lengths that are odd multiples of ¼ RF wavelength should be avoided. These results have important implications for the design of EEG electrodes and MRI studies as they help to minimize the risk to patients undergoing MRI with EEG electrodes in place.
Tobias Frank, Axel Krieger, Simon Leonard, Niravkumar A Patel, and Junichi Tokuda. 2017. “ROS-IGTL-Bridge: An Open Network Interface for Image-Guided Therapy using the ROS Environment.” Int J Comput Assist Radiol Surg, 12, 8, Pp. 1451-60.Abstract
PURPOSE: With the growing interest in advanced image-guidance for surgical robot systems, rapid integration and testing of robotic devices and medical image computing software are becoming essential in the research and development. Maximizing the use of existing engineering resources built on widely accepted platforms in different fields, such as robot operating system (ROS) in robotics and 3D Slicer in medical image computing could simplify these tasks. We propose a new open network bridge interface integrated in ROS to ensure seamless cross-platform data sharing. METHODS: A ROS node named ROS-IGTL-Bridge was implemented. It establishes a TCP/IP network connection between the ROS environment and external medical image computing software using the OpenIGTLink protocol. The node exports ROS messages to the external software over the network and vice versa simultaneously, allowing seamless and transparent data sharing between the ROS-based devices and the medical image computing platforms. RESULTS: Performance tests demonstrated that the bridge could stream transforms, strings, points, and images at 30 fps in both directions successfully. The data transfer latency was <1.2 ms for transforms, strings and points, and 25.2 ms for color VGA images. A separate test also demonstrated that the bridge could achieve 900 fps for transforms. Additionally, the bridge was demonstrated in two representative systems: a mock image-guided surgical robot setup consisting of 3D slicer, and Lego Mindstorms with ROS as a prototyping and educational platform for IGT research; and the smart tissue autonomous robot surgical setup with 3D Slicer. CONCLUSION: The study demonstrated that the bridge enabled cross-platform data sharing between ROS and medical image computing software. This will allow rapid and seamless integration of advanced image-based planning/navigation offered by the medical image computing software such as 3D Slicer into ROS-based surgical robot systems.
Isaiah Norton, Walid Ibn Essayed, Fan Zhang, Sonia Pujol, Alex Yarmarkovich, Alexandra J Golby, Gordon Kindlmann, Demian Wasserman, Raul San Jose Estepar, Yogesh Rathi, Steve Pieper, Ron Kikinis, Hans J Johnson, Carl-Fredrik Westin, and Lauren J O'Donnell. 2017. “SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research.” Cancer Res, 77, 21, Pp. e101-e103.Abstract
Diffusion MRI (dMRI) is the only noninvasive method for mapping white matter connections in the brain. We describe SlicerDMRI, a software suite that enables visualization and analysis of dMRI for neuroscientific studies and patient-specific anatomic assessment. SlicerDMRI has been successfully applied in multiple studies of the human brain in health and disease, and here, we especially focus on its cancer research applications. As an extension module of the 3D Slicer medical image computing platform, the SlicerDMRI suite enables dMRI analysis in a clinically relevant multimodal imaging workflow. Core SlicerDMRI functionality includes diffusion tensor estimation, white matter tractography with single and multi-fiber models, and dMRI quantification. SlicerDMRI supports clinical DICOM and research file formats, is open-source and cross-platform, and can be installed as an extension to 3D Slicer (www.slicer.org). More information, videos, tutorials, and sample data are available at dmri.slicer.org Cancer Res; 77(21); e101-3. ©2017 AACR.
K Fischer, S Ohori, FC Meral, M Uehara, S Giannini, T Ichimura, RN Smith, FA Jolesz, I Guleria, Y. Zhang, PJ White, NJ McDannold, K Hoffmeister, MM Givertz, and R Abdi. 2017. “Testing the Efficacy of Contrast-Enhanced Ultrasound in Detecting Transplant Rejection using a Murine Model of Heart Transplantation.” Am J Transplant, 17, 7, Pp. 1791-1801.Abstract
One of the key unmet needs to improve long-term outcomes of heart transplantation is to develop accurate, noninvasive, and practical diagnostic tools to detect transplant rejection. Early intragraft inflammation and endothelial cell injuries occur prior to advanced transplant rejection. We developed a novel diagnostic imaging platform to detect early declines in microvascular perfusion (MP) of cardiac transplants using contrast-enhanced ultrasonography (CEUS). The efficacy of CEUS in detecting transplant rejection was tested in a murine model of heart transplants, a standard preclinical model of solid organ transplant. As compared to the syngeneic groups, a progressive decline in MP was demonstrated in the allografts undergoing acute transplant rejection (40%, 64%, and 92% on days 4, 6, and 8 posttransplantation, respectively) and chronic rejection (33%, 33%, and 92% on days 5, 14, and 30 posttransplantation, respectively). Our perfusion studies showed restoration of MP following antirejection therapy, highlighting its potential to help monitor efficacy of antirejection therapy. Our data suggest that early endothelial cell injury and platelet aggregation contributed to the early MP decline observed in the allografts. High-resolution MP mapping may allow for noninvasive detection of heart transplant rejection. The data presented have the potential to help in the development of next-generation imaging approaches to diagnose transplant rejection.
Dimitris Mitsouras, Thomas C Lee, Peter Liacouras, Ciprian N Ionita, Todd Pietilla, Stephan E. Maier, and Robert V. Mulkern. 2017. “Three-dimensional Printing of MRI-visible Phantoms and MR Image-guided Therapy Simulation.” Magn Reson Med, 77, 2, Pp. 613-22.Abstract

PURPOSE: To demonstrate the use of anatomic MRI-visible three-dimensional (3D)-printed phantoms and to assess process accuracy and material MR signal properties. METHODS: A cervical spine model was generated from computed tomography (CT) data and 3D-printed using an MR signal-generating material. Printed phantom accuracy and signal characteristics were assessed using 120 kVp CT and 3 Tesla (T) MR imaging. The MR relaxation rates and diffusion coefficient of the fabricated phantom were measured and (1) H spectra were acquired to provide insight into the nature of the proton signal. Finally, T2 -weighted imaging was performed during cryoablation of the model. RESULTS: The printed model produced a CT signal of 102 ± 8 Hounsfield unit, and an MR signal roughly 1/3(rd) that of saline in short echo time/short repetition time GRE MRI (456 ± 36 versus 1526 ± 121 arbitrary signal units). Compared with the model designed from the in vivo CT scan, the printed model differed by 0.13 ± 0.11 mm in CT, and 0.62 ± 0.28 mm in MR. The printed material had T2 ∼32 ms, T2*∼7 ms, T1 ∼193 ms, and a very small diffusion coefficient less than olive oil. MRI monitoring of the cryoablation demonstrated iceball formation similar to an in vivo procedure. CONCLUSION: Current 3D printing technology can be used to print anatomically accurate phantoms that can be imaged by both CT and MRI. Such models can be used to simulate MRI-guided interventions such as cryosurgeries. Future development of the proposed technique can potentially lead to printed models that depict different tissues and anatomical structures with different MR signal characteristics. 

Mohsen Ghafoorian, Alireza Mehrtash, Tina Kapur, Nico Karssemeijer, Elena Marchiori, Mehran Pesteie, Charles RG Guttmann, Frank-Erik de Leeuw, Clare MC Tempany, Bram van Ginneken, Andriy Fedorov, Purang Abolmaesumi, Bram Plate, and William M Wells. 2017. “Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation.” Int Conf Med Image Comput Comput Assist Interv 20 (Pt3), Pp. 516-24.Abstract
Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, (1) How much data from the new domain is required for a decent adaptation of the original network?; and, (2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset. The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.
Ghafoorian MICCAI 2017
Walid I Essayed, Fan Zhang, Prashin Unadkat, Rees G Cosgrove, Alexandra J Golby, and Lauren J O'Donnell. 2017. “White Matter Tractography for Neurosurgical Planning: A Topography-based Review of the Current State of the Art.” Neuroimage Clin, 15, Pp. 659-72.Abstract
We perform a review of the literature in the field of white matter tractography for neurosurgical planning, focusing on those works where tractography was correlated with clinical information such as patient outcome, clinical functional testing, or electro-cortical stimulation. We organize the review by anatomical location in the brain and by surgical procedure, including both supratentorial and infratentorial pathologies, and excluding spinal cord applications. Where possible, we discuss implications of tractography for clinical care, as well as clinically relevant technical considerations regarding the tractography methods. We find that tractography is a valuable tool in variable situations in modern neurosurgery. Our survey of recent reports demonstrates multiple potentially successful applications of white matter tractography in neurosurgery, with progress towards overcoming clinical challenges of standardization and interpretation.
Pelin Aksit Ciris, Cheng-Chieh Cheng, Chang-Sheng Mei, Lawrence P. Panych, and Bruno Madore. 2017. “Dual-Pathway Sequences for MR Thermometry: When and Where to use Them.” Magn Reson Med, 77, 3, Pp. 1193-200.Abstract

PURPOSE: Dual-pathway sequences have been proposed to help improve the temperature-to-noise ratio (TNR) in MR thermometry. The present work establishes how much of an improvement these so-called "PSIF-FISP" sequences may bring in various organs and tissues. METHODS: Simulations and TNR calculations were validated against analytical equations, phantom, abdomen, and brain scans. Relative TNRs for PSIF-FISP, as compared to a dual-FISP reference standard, were calculated for flip angle (FA) = 1 to 85 º and repetition time (TR) = 6 to 60 ms, for gray matter, white matter, cervix, endometrium, myometrium, prostate, kidney medulla and cortex, bone marrow, pancreas, spleen, muscle, and liver tissues. RESULTS: PSIF-FISP was TNR superior in the kidney, pelvis, spleen, or gray matter at most tested TR and FA settings, and benefits increased at shorter TRs. PSIF-FISP was TNR superior in other tissues, e.g., liver, muscle, pancreas, for only short TR settings (20 ms or less). The TNR benefits of PSIF-FISP increased slightly with FA, and strongly with decreasing TR. Up to two- to three-fold reductions in TR with 20% TNR gains were achievable. In any given tissue, TNR performance is expected to further improve with heating, due to changes in relaxation rates. CONCLUSION: Dual-pathway PSIF-FISP can improve TNR and acquisition speed over standard gradient-recalled echo sequences, but optimal acquisition parameters are tissue dependent. Magn Reson Med 77:1193-1200, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Tzu-Cheng Chao, Jr-yuan George Chiou, Stephan E. Maier, and Bruno Madore. 2017. “Fast Diffusion Imaging with High Angular Resolution.” Magn Reson Med, 77, 2, Pp. 696-706.Abstract

PURPOSE: High angular resolution diffusion imaging (HARDI) is a well-established method to help reveal the architecture of nerve bundles, but long scan times and geometric distortions inherent to echo planar imaging (EPI) have limited its integration into clinical protocols. METHODS: A fast imaging method is proposed here that combines accelerated multishot diffusion imaging (AMDI), multiplexed sensitivity encoding (MUSE), and crossing fiber angular resolution of intravoxel structure (CFARI) to reduce spatial distortions and reduce total scan time. A multishot EPI sequence was used to improve geometrical fidelity as compared to a single-shot EPI acquisition, and acceleration in both k-space and diffusion sampling enabled reductions in scan time. The method is regularized and self-navigated for motion correction. Seven volunteers were scanned in this study, including four with volumetric whole brain acquisitions. RESULTS: The average similarity of microstructural orientations between undersampled datasets and their fully sampled counterparts was above 85%, with scan times below 5 min for whole-brain acquisitions. Up to 2.7-fold scan time acceleration along with four-fold distortion reduction was achieved. CONCLUSION: The proposed imaging strategy can generate HARDI results with relatively good geometrical fidelity and low scan duration, which may help facilitate the transition of HARDI from a successful research tool to a practical clinical one. Magn Reson Med, 2016. © 2016 Wiley Periodicals, Inc.

2016
Mao Li, Karol Miller, Grand Roman Joldes, Ron Kikinis, and Adam Wittek. 2016. “Biomechanical Model for Computing Deformations for Whole-body Image Registration: A Meshless Approach.” Int J Numer Method Biomed Eng, 32, 12.Abstract

Patient-specific biomechanical models have been advocated as a tool for predicting deformations of soft body organs/tissue for medical image registration (aligning two sets of images) when differences between the images are large. However, complex and irregular geometry of the body organs makes generation of patient-specific biomechanical models very time-consuming. Meshless discretisation has been proposed to solve this challenge. However, applications so far have been limited to 2D models and computing single organ deformations. In this study, 3D comprehensive patient-specific nonlinear biomechanical models implemented using meshless Total Lagrangian explicit dynamics algorithms are applied to predict a 3D deformation field for whole-body image registration. Unlike a conventional approach that requires dividing (segmenting) the image into non-overlapping constituents representing different organs/tissues, the mechanical properties are assigned using the fuzzy c-means algorithm without the image segmentation. Verification indicates that the deformations predicted using the proposed meshless approach are for practical purposes the same as those obtained using the previously validated finite element models. To quantitatively evaluate the accuracy of the predicted deformations, we determined the spatial misalignment between the registered (i.e. source images warped using the predicted deformations) and target images by computing the edge-based Hausdorff distance. The Hausdorff distance-based evaluation determines that our meshless models led to successful registration of the vast majority of the image features. Copyright © 2016 John Wiley & Sons, Ltd.

Pelin A Ciris, Mukund Balasubramanian, Antonio L Damato, Ravi T Seethamraju, Clare M Tempany-Afdhal, Robert V. Mulkern, and Akila N Viswanathan. 2016. “Characterizing Gradient Echo Signal Decays in Gynecologic Cancers at 3T using a Gaussian Augmentation of the Monoexponential (GAME) Model.” J Magn Reson Imaging, 44, 4, Pp. 1020-30.Abstract

PURPOSE: To assess whether R2* mapping with a standard Monoexponential (ME) or a Gaussian Augmentation of the Monoexponential (GAME) decay model better characterizes gradient-echo signal decays in gynecological cancers after external beam radiation therapy at 3T, and evaluate implications of modeling for noninvasive identification of intratumoral hypoxia. MATERIALS AND METHODS: Multi-gradient-echo signals were acquired on 25 consecutive patients with gynecologic cancers and three healthy participants during inhalation of different oxygen concentrations at 3T. Data were fitted with both ME and GAME models. Models were compared using F-tests in tumors and muscles in patients, muscles, cervix, and uterus in healthy participants, and across oxygenation levels. RESULTS: GAME significantly improved fitting over ME (P < 0.05): Improvements with GAME covered 34% of tumor regions-of-interest on average, ranging from 6% (of a vaginal tumor) to 68% (of a cervical tumor) in individual tumors. Improvements with GAME were more prominent in areas that would be assumed hypoxic based on ME alone, reaching 90% as ME R2* approached 100 Hz. Gradient echo decay parameters at different oxygenation levels were not significantly different (P = 0.81). CONCLUSION: R2* may prove sensitive to hypoxia; however, inaccurate representations of underlying data may limit the success of quantitative assessments. Although the degree to which R2 or σ values correlate with hypoxia remains unknown, improved characterization with GAME increases the potential for determining any correlates of fit parameters with biomarkers, such as oxygenation status. J. MAGN. RESON. IMAGING 2016;44:1020-1030.

Jeffrey P Guenette, Nathan Himes, Andreas A Giannopoulos, Tatiana Kelil, Dimitris Mitsouras, and Thomas C Lee. 2016. “Computer-Based Vertebral Tumor Cryoablation Planning and Procedure Simulation Involving Two Cases using MRI-Visible 3D Printing and Advanced Visualization.” AJR Am J Roentgenol, 207, 5, Pp. 1128-31.Abstract

OBJECTIVE: We report the development and use of MRI-compatible and MRI-visible 3D printed models in conjunction with advanced visualization software models to plan and simulate safe access routes to achieve a theoretic zone of cryoablation for percutaneous image-guided treatment of a C7 pedicle osteoid osteoma and an L1 lamina osteoblastoma. Both models altered procedural planning and patient care. CONCLUSION: Patient-specific MRI-visible models can be helpful in planning complex percutaneous image-guided cryoablation procedures.

Melissa Anne Mallory, Katya Losk, Kristen Camuso, Stephanie Caterson, Suniti Nimbkar, and Mehra Golshan. 2016. “Does "Two is Better Than One" Apply to Surgeons? Comparing Single-Surgeon Versus Co-surgeon Bilateral Mastectomies.” Ann Surg Oncol, 23, 4, Pp. 1111-6.Abstract
BACKGROUND: Bilateral mastectomies (BM) are traditionally performed by single surgeons (SS); a co-surgeon (CS) technique, where each surgeon concurrently performs a unilateral mastectomy, offers an alternative approach. We examined differences in general surgery time (GST), overall surgery time (OST), and patient complications for BM performed by CS and SS. METHODS: Patients undergoing BM with tissue expander reconstruction (BMTR) between January 2010 and May 2014 at our center were identified through operative case logs. GST (incision to end of BM procedure), reconstruction duration (RST) (plastic surgery start to end of reconstruction) and OST (OST = GST + RST) was calculated. Patient age, presence/stage of cancer, breast weight, axillary procedure performed, and 30-day postoperative complications were extracted from medical records. Differences in GST and OST between CS and SS cases were assessed with a t test. A multivariate linear regression was fit to identify factors associated with GST. RESULTS: A total of 116 BMTR cases were performed [CS, n = 67 (57.8 %); SS, n = 49 (42.2 %)]. Demographic characteristics did not differ between groups. GST and OST were significantly shorter for CS cases, 75.8 versus 116.8 min, p < .0001, and 255.2 versus 278.3 min, p = .005, respectively. Presence of a CS significantly reduces BMTR time (β = -38.82, p < .0001). Breast weight (β = 0.0093, p = .03) and axillary dissection (β = 28.69, p = .0003) also impacted GST. CONCLUSIONS: The CS approach to BMTR reduced both GST and OST; however, the degree of time savings (35.1 and 8.3 %, respectively) was less than hypothesized. A larger study is warranted to better characterize time, cost, and outcomes of the CS-approach for BM.
Fan Zhang, Peter Savadjev, Weidong Cai, Yang Song, Ragini Verma, Carl-Fredrik Westin, and Lauren J O'Donnell. 2016. “Fiber Clustering Based White Matter Connectivity Analysis for Prediction of Autism Spectrum Disorder using Diffusion Tensor Imaging.” In IEEE International Symposium on Biomedical Imaging, Pp. 564-7.Abstract

Autism Spectrum Disorder (ASD) has been suggested to associate with alterations 
in brain connectivity. In this study, we focus on a fiber clustering tractography segmentation 
strategy to observe white matter connectivity alterations in ASD. Compared to another 
popular parcellation-based approach for tractography segmentation based on cortical 
regions, we hypothesized that the clustering-based method could provide a more 
anatomically correspondent division of white matter. We applied this strategy to conduct a population-based group statistical analysis for the automated prediction of ASD. We obtained a maximum classification accuracy of 81.33% be- tween ASDs and controls, compared to the results of 78.00% from the parcellation-based method.

Zhang ISBI 2016 Paper
Frank Preiswerk, Matthew Toews, Cheng-Chieh Cheng, Jr-yuan George Chiou, Chang-Sheng Mei, Lena F Schaefer, W. Scott Hoge, Benjamin M Schwartz, Lawrence P Panych, and Bruno Madore. 2016. “Hybrid MRI Ultrasound Acquisitions, and Scannerless Real-time Imaging.” Magn Reson Med, 78, 3, Pp. 897-908.Abstract

PURPOSE: To combine MRI, ultrasound, and computer science methodologies toward generating MRI contrast at the high frame rates of ultrasound, inside and even outside the MRI bore. METHODS: A small transducer, held onto the abdomen with an adhesive bandage, collected ultrasound signals during MRI. Based on these ultrasound signals and their correlations with MRI, a machine-learning algorithm created synthetic MR images at frame rates up to 100 per second. In one particular implementation, volunteers were taken out of the MRI bore with the ultrasound sensor still in place, and MR images were generated on the basis of ultrasound signal and learned correlations alone in a "scannerless" manner. RESULTS: Hybrid ultrasound-MRI data were acquired in eight separate imaging sessions. Locations of liver features, in synthetic images, were compared with those from acquired images: The mean error was 1.0 pixel (2.1 mm), with best case 0.4 and worst case 4.1 pixels (in the presence of heavy coughing). For results from outside the bore, qualitative validation involved optically tracked ultrasound imaging with/without coughing. CONCLUSION: The proposed setup can generate an accurate stream of high-speed MR images, up to 100 frames per second, inside or even outside the MR bore. Magn Reson Med, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

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